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Although there are widely used methods as Genetic Algorithms, Fuzzy Logic and Artificial Neural Network, the Optimization Based Tools are considered the future of the systems of information. This issue is about Artificial Neural Network (ANN) used in Short Term Load Forecast (STLF). It proposes that the method is valid to predict STLF and how important it is on demand scheduling, contingency analysis,...
This paper presents a deep analysis of literature on the problems of optimization of parameters and structure of the neural networks and the basic disadvantages that are present in the observed algorithms and methods. As a result, there is suggested a new algorithm for neural network structure optimization, which is free of the major shortcomings of other algorithms. The paper describes a detailed...
Electrical load forecasting is essential in the field of power systems to enhance the operation and economical utilization In this paper, a combined approaches of artificial neural networks (ANN) with particle-swarm-optimization (PSO) and genetic algorithm optimization (GA) for short and mid-term load forecasting is developed. The model identifies the relationship among load, temperature and humidity...
On the side of enhancing the execution of skills, specialists in sports are adopting analysis of kinematics to correct actions of an athlete. By means of technological resources used to measure physical variables and to supply relevant data to trainers, results related to improvements on athletes' performance are being achieved. In this context, this work uses the Radial Basis Function Neural Networks...
Electrical discharge abrasive grinding (EDAG) of ferrous alloys such as high speed steel and high carbon steel, using diamond abrasive demonstrates poor machining performance. The chemical affinity of diamond towards the ferrous alloys is the main reason for the same. In the present research, the performance of cubic boron nitride (CBN) abrasive has been investigated. The parametric analysis, modeling...
Advanced Driving Assitance Systems (ADAS) cover a wide range of systems that aim to provide increasingly a safe and efficient driving. Many of these systems are endowed with some intelligent skills which are, in many cases, addressed by means of Soft Computing (SC) paradigms like Neural Networks (NN) or fuzzy systems among others. However, SC algorithms require normally large computational resources...
This paper presents the implementation of an artificial intelligence strategy genetic algorithm (GA) in the photovoltaic (PV) connected cascaded h-bridge multilevel inverter (CHB-MLI). In the presence of the worst maximum power point (MPP) due to temperature or irradiance mismatches, a voltage imbalance condition occurs in the PV systems. Here, genetic algorithm is used to find the most optimized...
Machine control using electroencephalography (EEG) based brain computer interfaces (BCI) has been extensively researched in the past decade. However, research is often based on event bound methods such as motor imagery. Despite being useful in medical applications, even bound methods limit users' operational capability while performing BCI control. To alleviate the said limitation, we explore a robot...
Technical Indicators (TIs) are not only useful tools to analyze and forecast the price of financial security, but also one of the well established applications currently in practice to perform high returns. Traders use TIs to generate trading buy/sell signals. In spite of their practical success, the main problem of a TI is to determine its appropriate parameters which help obtain the most accurate...
Advances in soft computing reshape the manufacturing industries to develop an integrated, self-adjusting manufacturing systems into dynamically scalable and highly distributed cost-efficient business model. Due to presence of uncertainty and inaccuracy in manufacturing processes, the various soft computing algorithms i.e. neural networks, fuzzy sets, genetic algorithms, ant colony optimization, adaptive...
Artificial neural network (ANN) was proposed as an effective method to help multipath ultrasonic flowmeter (UFM) reduce its measurement error when determining the flowrate of complex flow field. However, the effectiveness of the ANN method heavily depends on the network architecture specified by the designer, and also the provided initial weights and layer biases. This hinders the ANN to be widely...
The solar photo voltaic (PV) systems manifest their utility in a number of ways and have emerged as one of the promising renewable sources of electrical power. Solar PV array has a non-linear characteristic. The voltage across the output terminals of the PV array and its internal resistance vary along with changes in the ambient conditions of temperature and insolation. As irradiation and temperature...
Recent dynamic advances in Telecom evolution calls for optimization of technologies, something which we continually learn from the nature. Adaptive intelligent machines, inspired from biological evolution, should emerge as cost-controlled, energy-efficient and sustainable alternatives for future generations. In this paper, we review the different evolving intelligent techniques implemented in various...
Fault diagnosis on Multilevel Inverter (MLI) has been a subject of research for about a decade. This paper is an attempt to deliver a performance analysis of Genetic Algorithm (GA) and the Modified Genetic Algorithm (MGA) working to optimize Artificial Neural Network (ANN) that trains itself on the fault detection and reconfiguration of the Cascaded Multilevel Inverters (CMLI). The open circuit (OC)...
This work presents a metaheuristic hybrid optimization technique developed to synthesize frequency selective surface (FSS) structures, composed of triangular patch elements and printed on FR-4 dielectric substrates for microwave filtering applications. The optimization technique is based on the combination of genetic algorithm (GA) and Multilayer Perceptron (MLP) artificial neural network (ANN). The...
A modified Hopfield Artificial Neural Network is proposed to solve effectively and efficiently Boolean Satisfiability (SAT) NP-hard problems. The proposed Neural Network is compared against other traditional methods employed in this field, such as Greedy SAT and Genetic Algorithms for SAT. The results show that the proposed network represents a good alternative given their output quality and response...
In this paper, Ant Lion Optimizer (ALO) was presented to train Multi-Layer Perceptron (MLP). ALO was used to find the weights and biases of the MLP to achieve a minimum error and a high classification rate. Four standard classification datasets were used to benchmark the performance of the proposed method. In addition, the performance of the proposed method were compared with three well-known optimization...
The purpose of this study is to develop an effective hybrid aerodynamic optimization technique in the compressible flow for a forward swept wing (FSW) by using artificial neural network (ANN). ANN is hybridized with a genetic optimization method. This new optimization technique is observed much faster than Genetic Algorithm (GA). By using the method presented in this study, the drag coefficient can...
The microelectronic industry is driven by the continuous demand for processing speed and capacity. To answer such demands, novel design paradigms target design automation. While digital design is mostly automatic, design automation in the analog domain is limited and mainly used for high-level synthesis. A promising solution to overcome limitations and constrains of automatic analog design is to involve...
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